Daily and seasonal variations of soil respiration from maize field under different water treatments in North China

Abstract

To further evaluate the effect of water stress on soil respiration (R_S), reveal the influencing factors of daily and seasonal R_S, and systematically evaluate and compare the sensibility of different machine learning algorithms (multiple nonlinear regression [MNR], support vector machine regression [SVR], backpropagation artificial neural network [BPNN]) to estimate R_S from a maize field under water stress condition, the field experiments were conducted within a maize field in Inner Mongolia, China, during the entire 2019 growing season. Various levels of deficit irrigation were conducted in the vegetative, reproductive, and mature stages. Our research indicated that soil CO₂ fluxes from 100% evapotranspiration treatment (Tr1) were significantly greater than various deficit irrigation treatments (Tr2, Tr3, Tr4) during each growth stage of summer maize. The cumulative soil CO₂ fluxes of Tr2, Tr3, and Tr4 decreased 24.8%, 30.3%, and 43.7% compared with Tr1, respectively. We determined that the drivers affecting the daily R_S were soil temperature at 5 cm depth (T_S,5) and soil surface temperature (T_SF), followed by water-filled porosity (WFPS) at 5 cm depth, but no significant correlations were observed at 25 cm depths. T_S,5 and T_SF also performed similar correlation with seasonal R_S with R greater than 0.753 among all water treatments, followed by chlorophyll content with R greater than 0.726. During the whole growing season, the BPNN model exhibited the best predicting result, and could explain the 60%–80% and 87.8% of the variations of R_S at the daily and seasonal scales, with root mean square error of 48.7–100.9 mg m⁻² h⁻¹ and 91.5 mg m⁻² h⁻¹, respectively. The SVR and MNR models could estimate the 47.9%–57% and 39.9%–52.1% of the daily R_S and 81.4% and 78.6% of the seasonal R_S, respectively. Overall, our study indicated the machine learning algorithms could be successfully applied to estimate R_S at daily and seasonal scales from a maize field under water stress condition.